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Multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using SNP markers
BACKGROUND: The amount of functional genomic information has been growing rapidly but remains largely unused in genomic selection. Genomic prediction and estimation using haplotypes in genome regions with functional elements such as all genes of the genome can be an approach to integrate functional...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4683770/ https://www.ncbi.nlm.nih.gov/pubmed/26678438 http://dx.doi.org/10.1186/s12863-015-0301-1 |
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author | Da, Yang |
author_facet | Da, Yang |
author_sort | Da, Yang |
collection | PubMed |
description | BACKGROUND: The amount of functional genomic information has been growing rapidly but remains largely unused in genomic selection. Genomic prediction and estimation using haplotypes in genome regions with functional elements such as all genes of the genome can be an approach to integrate functional and structural genomic information for genomic selection. Towards this goal, this article develops a new haplotype approach for genomic prediction and estimation. RESULTS: A multi-allelic haplotype model treating each haplotype as an ‘allele’ was developed for genomic prediction and estimation based on the partition of a multi-allelic genotypic value into additive and dominance values. Each additive value is expressed as a function of h − 1 additive effects, where h = number of alleles or haplotypes, and each dominance value is expressed as a function of h(h − 1)/2 dominance effects. For a sample of q individuals, the limit number of effects is 2q − 1 for additive effects and is the number of heterozygous genotypes for dominance effects. Additive values are factorized as a product between the additive model matrix and the h − 1 additive effects, and dominance values are factorized as a product between the dominance model matrix and the h(h − 1)/2 dominance effects. Genomic additive relationship matrix is defined as a function of the haplotype model matrix for additive effects, and genomic dominance relationship matrix is defined as a function of the haplotype model matrix for dominance effects. Based on these results, a mixed model implementation for genomic prediction and variance component estimation that jointly use haplotypes and single markers is established, including two computing strategies for genomic prediction and variance component estimation with identical results. CONCLUSION: The multi-allelic genetic partition fills a theoretical gap in genetic partition by providing general formulations for partitioning multi-allelic genotypic values and provides a haplotype method based on the quantitative genetics model towards the utilization of functional and structural genomic information for genomic prediction and estimation. |
format | Online Article Text |
id | pubmed-4683770 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-46837702015-12-19 Multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using SNP markers Da, Yang BMC Genet Methodology Article BACKGROUND: The amount of functional genomic information has been growing rapidly but remains largely unused in genomic selection. Genomic prediction and estimation using haplotypes in genome regions with functional elements such as all genes of the genome can be an approach to integrate functional and structural genomic information for genomic selection. Towards this goal, this article develops a new haplotype approach for genomic prediction and estimation. RESULTS: A multi-allelic haplotype model treating each haplotype as an ‘allele’ was developed for genomic prediction and estimation based on the partition of a multi-allelic genotypic value into additive and dominance values. Each additive value is expressed as a function of h − 1 additive effects, where h = number of alleles or haplotypes, and each dominance value is expressed as a function of h(h − 1)/2 dominance effects. For a sample of q individuals, the limit number of effects is 2q − 1 for additive effects and is the number of heterozygous genotypes for dominance effects. Additive values are factorized as a product between the additive model matrix and the h − 1 additive effects, and dominance values are factorized as a product between the dominance model matrix and the h(h − 1)/2 dominance effects. Genomic additive relationship matrix is defined as a function of the haplotype model matrix for additive effects, and genomic dominance relationship matrix is defined as a function of the haplotype model matrix for dominance effects. Based on these results, a mixed model implementation for genomic prediction and variance component estimation that jointly use haplotypes and single markers is established, including two computing strategies for genomic prediction and variance component estimation with identical results. CONCLUSION: The multi-allelic genetic partition fills a theoretical gap in genetic partition by providing general formulations for partitioning multi-allelic genotypic values and provides a haplotype method based on the quantitative genetics model towards the utilization of functional and structural genomic information for genomic prediction and estimation. BioMed Central 2015-12-18 /pmc/articles/PMC4683770/ /pubmed/26678438 http://dx.doi.org/10.1186/s12863-015-0301-1 Text en © Da. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Da, Yang Multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using SNP markers |
title | Multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using SNP markers |
title_full | Multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using SNP markers |
title_fullStr | Multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using SNP markers |
title_full_unstemmed | Multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using SNP markers |
title_short | Multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using SNP markers |
title_sort | multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using snp markers |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4683770/ https://www.ncbi.nlm.nih.gov/pubmed/26678438 http://dx.doi.org/10.1186/s12863-015-0301-1 |
work_keys_str_mv | AT dayang multiallelichaplotypemodelbasedongeneticpartitionforgenomicpredictionandvariancecomponentestimationusingsnpmarkers |